




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCamelCase							:						str					       =  logging.get_logger(__name__)

# General docstring
lowerCamelCase							:						List[str]					       =  "RegNetConfig"

# Base docstring
lowerCamelCase							:						Tuple					       =  "facebook/regnet-y-040"
lowerCamelCase							:						Dict					       =  [1, 1_088, 7, 7]

# Image classification docstring
lowerCamelCase							:						str					       =  "facebook/regnet-y-040"
lowerCamelCase							:						Union[str, Any]					       =  "tabby, tabby cat"

lowerCamelCase							:						List[str]					       =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    List[str]		,				A_   :    int		,				A_   :    int = 3		,				A_   :    int = 1		,				A_   :    int = 1		,				A_   :    Optional[str] = "relu"		,				**A_   :    int		,				)  ->      Dict:

       """simple docstring"""






       super().__init__(**A_    )
       # The padding and conv has been verified in
       # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
       lowerCamelCase_							=       tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2    )
       lowerCamelCase_							=       tf.keras.layers.ConvaD(
           filters=A_		,				kernel_size=A_		,				strides=A_		,				padding='VALID'		,				groups=A_		,				use_bias=A_		,				name='convolution'		,				)
       lowerCamelCase_							=       tf.keras.layers.BatchNormalization(epsilon=1E-5		,				momentum=0.9		,				name='normalization'    )
       lowerCamelCase_							=       ACTaFN[activation] if activation is not None else tf.identity





 def 							a__					(							self   :    Dict		,				A_   :    Any    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.convolution(self.padding(A_    )    )
       lowerCamelCase_							=       self.normalization(A_    )
       lowerCamelCase_							=       self.activation(A_    )
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Optional[int]		,				A_   :    RegNetConfig		,				**A_   :    List[str]    )  ->      Any:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       config.num_channels
       lowerCamelCase_							=       TFRegNetConvLayer(
           out_channels=config.embedding_size		,				kernel_size=3		,				stride=2		,				activation=config.hidden_act		,				name='embedder'		,				)





 def 							a__					(							self   :    Any		,				A_   :    str    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       shape_list(A_    )[1]
       if tf.executing_eagerly() and num_channels != self.num_channels:
             raise ValueError(
                 'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'    )

       # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
       # So change the input format from `NCHW` to `NHWC`.
       # shape = (batch_size, in_height, in_width, in_channels=num_channels)
       lowerCamelCase_							=       tf.transpose(A_		,				perm=(0, 2, 3, 1)    )
       lowerCamelCase_							=       self.embedder(A_    )
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Tuple		,				A_   :    int		,				A_   :    int = 2		,				**A_   :    str    )  ->      Optional[int]:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       tf.keras.layers.ConvaD(
           filters=A_		,				kernel_size=1		,				strides=A_		,				use_bias=A_		,				name='convolution'    )
       lowerCamelCase_							=       tf.keras.layers.BatchNormalization(epsilon=1E-5		,				momentum=0.9		,				name='normalization'    )





 def 							a__					(							self   :    Any		,				A_   :    tf.Tensor		,				A_   :    bool = False    )  ->      tf.Tensor:

       """simple docstring"""






       return self.normalization(self.convolution(A_    )		,				training=A_    )







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Any		,				A_   :    int		,				A_   :    int		,				**A_   :    Dict    )  ->      Optional[int]:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_		,				name='pooler'    )
       lowerCamelCase_							=       [
           tf.keras.layers.ConvaD(filters=A_		,				kernel_size=1		,				activation='relu'		,				name='attention.0'    ),
           tf.keras.layers.ConvaD(filters=A_		,				kernel_size=1		,				activation='sigmoid'		,				name='attention.2'    ),
       ]





 def 							a__					(							self   :    List[Any]		,				A_   :    Any    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       self.pooler(A_    )
       for layer_module in self.attention:
             lowerCamelCase_							=       layer_module(A_    )
       lowerCamelCase_							=       hidden_state * pooled
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    List[Any]		,				A_   :    RegNetConfig		,				A_   :    int		,				A_   :    int		,				A_   :    int = 1		,				**A_   :    Optional[Any]    )  ->      Any:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       in_channels != out_channels or stride != 1
       lowerCamelCase_							=       max(1		,				out_channels // config.groups_width    )
       lowerCamelCase_							=       (
           TFRegNetShortCut(A_		,				stride=A_		,				name='shortcut'    )
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear'		,				name='shortcut'    )
       )
       # `self.layers` instead of `self.layer` because that is a reserved argument.
       lowerCamelCase_							=       [
           TFRegNetConvLayer(A_		,				kernel_size=1		,				activation=config.hidden_act		,				name='layer.0'    ),
           TFRegNetConvLayer(
               A_		,				stride=A_		,				groups=A_		,				activation=config.hidden_act		,				name='layer.1'    ),
           TFRegNetConvLayer(A_		,				kernel_size=1		,				activation=A_		,				name='layer.2'    ),
       ]
       lowerCamelCase_							=       ACTaFN[config.hidden_act]





 def 							a__					(							self   :    Any		,				A_   :    List[str]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       hidden_state
       for layer_module in self.layers:
             lowerCamelCase_							=       layer_module(A_    )
       lowerCamelCase_							=       self.shortcut(A_    )
       hidden_state += residual
       lowerCamelCase_							=       self.activation(A_    )
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Dict		,				A_   :    RegNetConfig		,				A_   :    int		,				A_   :    int		,				A_   :    int = 1		,				**A_   :    Optional[Any]    )  ->      Tuple:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       in_channels != out_channels or stride != 1
       lowerCamelCase_							=       max(1		,				out_channels // config.groups_width    )
       lowerCamelCase_							=       (
           TFRegNetShortCut(A_		,				stride=A_		,				name='shortcut'    )
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear'		,				name='shortcut'    )
       )
       lowerCamelCase_							=       [
           TFRegNetConvLayer(A_		,				kernel_size=1		,				activation=config.hidden_act		,				name='layer.0'    ),
           TFRegNetConvLayer(
               A_		,				stride=A_		,				groups=A_		,				activation=config.hidden_act		,				name='layer.1'    ),
           TFRegNetSELayer(A_		,				reduced_channels=int(round(in_channels / 4    )    )		,				name='layer.2'    ),
           TFRegNetConvLayer(A_		,				kernel_size=1		,				activation=A_		,				name='layer.3'    ),
       ]
       lowerCamelCase_							=       ACTaFN[config.hidden_act]





 def 							a__					(							self   :    Any		,				A_   :    Union[str, Any]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       hidden_state
       for layer_module in self.layers:
             lowerCamelCase_							=       layer_module(A_    )
       lowerCamelCase_							=       self.shortcut(A_    )
       hidden_state += residual
       lowerCamelCase_							=       self.activation(A_    )
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Any		,				A_   :    RegNetConfig		,				A_   :    int		,				A_   :    int		,				A_   :    int = 2		,				A_   :    int = 2		,				**A_   :    Tuple    )  ->      List[Any]:

       """simple docstring"""






       super().__init__(**A_    )

       lowerCamelCase_							=       TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
       lowerCamelCase_							=       [
           # downsampling is done in the first layer with stride of 2
           layer(A_		,				A_		,				A_		,				stride=A_		,				name='layers.0'    ),
           *[layer(A_		,				A_		,				A_		,				name=f"""layers.{i+1}"""    ) for i in range(depth - 1    )],
       ]





 def 							a__					(							self   :    str		,				A_   :    List[Any]    )  ->      List[str]:

       """simple docstring"""






       for layer_module in self.layers:
             lowerCamelCase_							=       layer_module(A_    )
       return hidden_state







class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 def __init__(							self   :    Tuple		,				A_   :    RegNetConfig		,				**A_   :    Any    )  ->      List[str]:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       []
       # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
       self.stages.append(
           TFRegNetStage(
               A_		,				config.embedding_size		,				config.hidden_sizes[0]		,				stride=2 if config.downsample_in_first_stage else 1		,				depth=config.depths[0]		,				name='stages.0'		,				)    )
       lowerCamelCase_							=       zip(config.hidden_sizes		,				config.hidden_sizes[1:]    )
       for i, ((in_channels, out_channels), depth) in enumerate(zip(A_		,				config.depths[1:]    )    ):
             self.stages.append(TFRegNetStage(A_		,				A_		,				A_		,				depth=A_		,				name=f"""stages.{i+1}"""    )    )





 def 							a__					(							self   :    Dict		,				A_   :    tf.Tensor		,				A_   :    bool = False		,				A_   :    bool = True    )  ->      TFBaseModelOutputWithNoAttention:

       """simple docstring"""






       lowerCamelCase_							=       () if output_hidden_states else None

       for stage_module in self.stages:
             if output_hidden_states:
                   lowerCamelCase_							=       hidden_states + (hidden_state,)

             lowerCamelCase_							=       stage_module(A_    )

       if output_hidden_states:
             lowerCamelCase_							=       hidden_states + (hidden_state,)

       if not return_dict:
             return tuple(v for v in [hidden_state, hidden_states] if v is not None    )

       return TFBaseModelOutputWithNoAttention(last_hidden_state=A_		,				hidden_states=A_    )







@keras_serializable
class     A(		tf.keras.layers.Layer  ):



 '''simple docstring'''


 UpperCamelCase		  =		RegNetConfig

 def __init__(							self   :    List[str]		,				A_   :    Optional[Any]		,				**A_   :    int    )  ->      Optional[int]:

       """simple docstring"""






       super().__init__(**A_    )
       lowerCamelCase_							=       config
       lowerCamelCase_							=       TFRegNetEmbeddings(A_		,				name='embedder'    )
       lowerCamelCase_							=       TFRegNetEncoder(A_		,				name='encoder'    )
       lowerCamelCase_							=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=A_		,				name='pooler'    )





 @unpack_inputs
 def 							a__					(							self   :    int		,				A_   :    tf.Tensor		,				A_   :    Optional[bool] = None		,				A_   :    Optional[bool] = None		,				A_   :    bool = False		,				)  ->      TFBaseModelOutputWithPoolingAndNoAttention:

       """simple docstring"""






       lowerCamelCase_							=       (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCamelCase_							=       return_dict if return_dict is not None else self.config.use_return_dict

       lowerCamelCase_							=       self.embedder(A_		,				training=A_    )

       lowerCamelCase_							=       self.encoder(
           A_		,				output_hidden_states=A_		,				return_dict=A_		,				training=A_    )

       lowerCamelCase_							=       encoder_outputs[0]
       lowerCamelCase_							=       self.pooler(A_    )

       # Change to NCHW output format have uniformity in the modules
       lowerCamelCase_							=       tf.transpose(A_		,				perm=(0, 3, 1, 2)    )
       lowerCamelCase_							=       tf.transpose(A_		,				perm=(0, 3, 1, 2)    )

       # Change the other hidden state outputs to NCHW as well
       if output_hidden_states:
             lowerCamelCase_							=       tuple([tf.transpose(A_		,				perm=(0, 3, 1, 2)    ) for h in encoder_outputs[1]]    )

       if not return_dict:
             return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=A_		,				pooler_output=A_		,				hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states		,				)







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		RegNetConfig
 UpperCamelCase		  =		'''regnet'''
 UpperCamelCase		  =		'''pixel_values'''

 @property
 def 							a__					(							self   :    int    )  ->      Dict:

       """simple docstring"""






       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)		,				dtype=tf.floataa    )}

lowerCamelCase							:						str					       =  r"\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowerCamelCase							:						Dict					       =  r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"







@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''				, UpperCamelCase				, )
class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Tuple		,				A_   :    RegNetConfig		,				*A_   :    Tuple		,				**A_   :    Any    )  ->      int:

       """simple docstring"""






       super().__init__(A_		,				*A_		,				**A_    )
       lowerCamelCase_							=       TFRegNetMainLayer(A_		,				name='regnet'    )





 @unpack_inputs
 @add_start_docstrings_to_model_forward(A_    )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC		,				output_type=A_		,				config_class=_CONFIG_FOR_DOC		,				modality='vision'		,				expected_output=_EXPECTED_OUTPUT_SHAPE		,				)
 def 							a__					(							self   :    List[str]		,				A_   :    tf.Tensor		,				A_   :    Optional[bool] = None		,				A_   :    Optional[bool] = None		,				A_   :    int=False		,				)  ->      Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:

       """simple docstring"""






       lowerCamelCase_							=       (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCamelCase_							=       return_dict if return_dict is not None else self.config.use_return_dict

       lowerCamelCase_							=       self.regnet(
           pixel_values=A_		,				output_hidden_states=A_		,				return_dict=A_		,				training=A_		,				)
       if not return_dict:
             return (outputs[0],) + outputs[1:]

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=outputs.last_hidden_state		,				pooler_output=outputs.pooler_output		,				hidden_states=outputs.hidden_states		,				)







@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''				, UpperCamelCase				, )
class     A(		UpperCamelCase				, UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    List[str]		,				A_   :    RegNetConfig		,				*A_   :    Optional[int]		,				**A_   :    int    )  ->      Optional[Any]:

       """simple docstring"""






       super().__init__(A_		,				*A_		,				**A_    )
       lowerCamelCase_							=       config.num_labels
       lowerCamelCase_							=       TFRegNetMainLayer(A_		,				name='regnet'    )
       # classification head
       lowerCamelCase_							=       [
           tf.keras.layers.Flatten(),
           tf.keras.layers.Dense(config.num_labels		,				name='classifier.1'    ) if config.num_labels > 0 else tf.identity,
       ]





 @unpack_inputs
 @add_start_docstrings_to_model_forward(A_    )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT		,				output_type=A_		,				config_class=_CONFIG_FOR_DOC		,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,				)
 def 							a__					(							self   :    Optional[int]		,				A_   :    tf.Tensor = None		,				A_   :    tf.Tensor = None		,				A_   :    bool = None		,				A_   :    bool = None		,				A_   :    List[Any]=False		,				)  ->      Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:

       """simple docstring"""






       lowerCamelCase_							=       (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowerCamelCase_							=       return_dict if return_dict is not None else self.config.use_return_dict

       lowerCamelCase_							=       self.regnet(
           A_		,				output_hidden_states=A_		,				return_dict=A_		,				training=A_    )

       lowerCamelCase_							=       outputs.pooler_output if return_dict else outputs[1]

       lowerCamelCase_							=       self.classifier[0](A_    )
       lowerCamelCase_							=       self.classifier[1](A_    )

       lowerCamelCase_							=       None if labels is None else self.hf_compute_loss(labels=A_		,				logits=A_    )

       if not return_dict:
             lowerCamelCase_							=       (logits,) + outputs[2:]
             return ((loss,) + output) if loss is not None else output

       return TFSequenceClassifierOutput(loss=A_		,				logits=A_		,				hidden_states=outputs.hidden_states    )










'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



